{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cyclical features in time series forecasting"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cyclical features play an important role in time series prediction because they capture recurring patterns or oscillations within a data set. These patterns repeat at fixed intervals, and the effective incorporation of cyclical features into a machine learning model requires careful preprocessing and feature engineering.\n",
    "\n",
    "Due to the circular nature of cyclical features, it is not recommended to use them directly as numerical inputs in a machine learning model. Instead, they should be encoded in a format that captures their cyclical behavior. There are several common encoding techniques:\n",
    "\n",
    "+ One-hot encoding: If the cyclical feature consists of distinct categories, such as seasons or months, one-hot encoding can be used. This approach creates binary variables for each category, allowing the model to understand the presence or absence of specific categories.\n",
    "\n",
    "+ Trigonometric coding: For periodic features such as time of day or day of the week, trigonometric functions such as sine and cosine can be used for coding. By mapping the cyclic feature onto a unit circle, these functions preserve the cyclic relationships. In addition, this method introduces only two additional features, making it an efficient coding technique.\n",
    "\n",
    "+ Basis functions: Basis functions are mathematical functions that span a vector space and can be used to represent other functions within that space. When using basis functions, the cyclic feature is transformed into a new set of features based on the selected basis functions. Some commonly used basis functions for encoding cyclic features include Fourier basis functions, B-spline basis functions, and Gaussian basis functions. B-splines are a way to approximate nonlinear functions using a piecewise combination of polynomials.\n",
    "\n",
    "By applying these encoding techniques, cyclic features can be effectively incorporated into a machine learning model, allowing it to capture and exploit the valuable recurring patterns present in time series data."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,184,212,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00b8d4; border-color: #00b8d4; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00b8d4;\"></i>\n",
    "    <b style=\"color: #00b8d4;\">&#9998 Note</b>\n",
    "</p>\n",
    "\n",
    "The following examples are inspired by <a href=\"https://scikit-learn.org/stable/auto_examples/applications/plot_cyclical_feature_engineering.html#sphx-glr-auto-examples-applications-plot-cyclical-feature-engineering-py\">Time-related feature engineering</a>, <a href=\"https://scikit-lego.netlify.app/preprocessing.html#Repeating-Basis-Function-Transformer\">scikit-lego’s documentation</a> and <a href=\"https://developer.nvidia.com/blog/three-approaches-to-encoding-time-information-as-features-for-ml-models/\">Three Approaches to Encoding Time Information as Features for ML Models By Eryk Lewinson</a>.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Libraries and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-28T14:52:40.040628Z",
     "start_time": "2022-09-28T14:52:38.284162Z"
    }
   },
   "outputs": [],
   "source": [
    "# Data manipulation\n",
    "# ==============================================================================\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Plots\n",
    "# ==============================================================================\n",
    "import matplotlib.pyplot as plt\n",
    "from skforecast.plot import set_dark_theme\n",
    "\n",
    "# Modelling and Forecasting\n",
    "# ==============================================================================\n",
    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
    "from sklearn.preprocessing import FunctionTransformer, OneHotEncoder, SplineTransformer\n",
    "from sklearn.compose import make_column_transformer\n",
    "from skforecast.recursive import ForecasterRecursive\n",
    "from skforecast.model_selection import TimeSeriesFold, backtesting_forecaster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>2.928244</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>4.866145</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>4.425159</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   y  month\n",
       "date                       \n",
       "2020-01-01  2.928244      1\n",
       "2020-01-02  4.866145      1\n",
       "2020-01-03  4.425159      1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data simulation\n",
    "# ==============================================================================\n",
    "np.random.seed(123)\n",
    "dates = pd.date_range(start=\"2020-01-01\", end=\"2023-12-31\")\n",
    "data = pd.DataFrame(index=dates)\n",
    "data.index.name = \"date\"\n",
    "data[\"day_idx\"] = range(len(data))\n",
    "data['month'] = data.index.month\n",
    "\n",
    "# Create the components that will be combined to get the target series\n",
    "signal_1 = 3 + 4 * np.sin(data[\"day_idx\"] / 365 * 2 * np.pi)\n",
    "signal_2 = 3 * np.sin(data[\"day_idx\"] / 365 * 4 * np.pi + 365 / 2)\n",
    "noise = np.random.normal(0, 0.85, len(data))\n",
    "y = signal_1 + signal_2 + noise\n",
    "\n",
    "data[\"y\"] = y\n",
    "data = data[[\"y\", \"month\"]]\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dates train : 2020-01-01 00:00:00 --- 2022-06-30 00:00:00  (n=912)\n",
      "Dates test  : 2022-07-01 00:00:00 --- 2023-12-31 00:00:00  (n=549)\n"
     ]
    }
   ],
   "source": [
    "# Split train-test\n",
    "# ==============================================================================\n",
    "end_train = '2022-06-30 23:59:00'\n",
    "data_train = data.loc[: end_train, :]\n",
    "data_test  = data.loc[end_train:, :]\n",
    "\n",
    "print(f\"Dates train : {data_train.index.min()} --- {data_train.index.max()}  (n={len(data_train)})\")\n",
    "print(f\"Dates test  : {data_test.index.min()} --- {data_test.index.max()}  (n={len(data_test)})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot time series\n",
    "# ==============================================================================\n",
    "set_dark_theme()\n",
    "fig, ax = plt.subplots(figsize=(7, 3))\n",
    "data_train['y'].plot(title=\"Time series\", label=\"train\", ax=ax)\n",
    "data_test['y'].plot(title=\"Time series\", label=\"test\", ax=ax)\n",
    "ax.legend();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month_1</th>\n",
       "      <th>month_2</th>\n",
       "      <th>month_3</th>\n",
       "      <th>month_4</th>\n",
       "      <th>month_5</th>\n",
       "      <th>month_6</th>\n",
       "      <th>month_7</th>\n",
       "      <th>month_8</th>\n",
       "      <th>month_9</th>\n",
       "      <th>month_10</th>\n",
       "      <th>month_11</th>\n",
       "      <th>month_12</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.928244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.866145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.425159</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            month_1  month_2  month_3  month_4  month_5  month_6  month_7  \\\n",
       "date                                                                        \n",
       "2020-01-01      1.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "2020-01-02      1.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "2020-01-03      1.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "            month_8  month_9  month_10  month_11  month_12         y  \n",
       "date                                                                  \n",
       "2020-01-01      0.0      0.0       0.0       0.0       0.0  2.928244  \n",
       "2020-01-02      0.0      0.0       0.0       0.0       0.0  4.866145  \n",
       "2020-01-03      0.0      0.0       0.0       0.0       0.0  4.425159  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One hot encoding of week_day and hour_day\n",
    "# ==============================================================================\n",
    "one_hot_encoder = make_column_transformer(\n",
    "                      (\n",
    "                          OneHotEncoder(sparse_output=False, drop='if_binary'),\n",
    "                          ['month'],\n",
    "                      ),\n",
    "                      remainder=\"passthrough\",\n",
    "                      verbose_feature_names_out=False,\n",
    "                  ).set_output(transform=\"pandas\")\n",
    "\n",
    "data_encoded_oh = one_hot_encoder.fit_transform(data)\n",
    "data_encoded_oh.head(3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cyclical encoding with sine/cosine transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>month</th>\n",
       "      <th>month_sin</th>\n",
       "      <th>month_cos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>2.928244</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.866025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>4.866145</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.866025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>4.425159</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.866025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>3.069222</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.866025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>4.021290</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.866025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   y  month  month_sin  month_cos\n",
       "date                                             \n",
       "2020-01-01  2.928244      1        0.5   0.866025\n",
       "2020-01-02  4.866145      1        0.5   0.866025\n",
       "2020-01-03  4.425159      1        0.5   0.866025\n",
       "2020-01-04  3.069222      1        0.5   0.866025\n",
       "2020-01-05  4.021290      1        0.5   0.866025"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cyclical encoding with sine/cosine transformation\n",
    "# ==============================================================================\n",
    "def sin_transformer(period):\n",
    "\t\"\"\"\n",
    "\tReturns a transformer that applies sine transformation to a variable using\n",
    "\tthe specified period.\n",
    "\t\"\"\"\n",
    "\treturn FunctionTransformer(lambda x: np.sin(x / period * 2 * np.pi))\n",
    "\n",
    "def cos_transformer(period):\n",
    "\t\"\"\"\n",
    "\tReturns a transformer that applies cosine transformation to a variable using\n",
    "\tthe specified period.\n",
    "\t\"\"\"\n",
    "\treturn FunctionTransformer(lambda x: np.cos(x / period * 2 * np.pi))\n",
    "\n",
    "data_encoded_sin_cos = data.copy()\n",
    "data_encoded_sin_cos[\"month_sin\"] = sin_transformer(12).fit_transform(data_encoded_sin_cos['month'])\n",
    "data_encoded_sin_cos[\"month_cos\"] = cos_transformer(12).fit_transform(data_encoded_sin_cos['month'])\n",
    "data_encoded_sin_cos.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot of the transformation\n",
    "# ==============================================================================\n",
    "fig, ax = plt.subplots(figsize=(4., 3.5))\n",
    "sp = ax.scatter(\n",
    "        data_encoded_sin_cos[\"month_sin\"],\n",
    "        data_encoded_sin_cos[\"month_cos\"],\n",
    "        c=data_encoded_sin_cos[\"month\"],\n",
    "        cmap='viridis'\n",
    "     )\n",
    "ax.set(\n",
    "    xlabel=\"sin(month)\",\n",
    "    ylabel=\"cos(month)\",\n",
    ")\n",
    "_ = fig.colorbar(sp)\n",
    "data_encoded_sin_cos = data_encoded_sin_cos.drop(columns='month')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## B-splines functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>2.928244</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>4.866145</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>4.425159</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   y  month  day_of_year\n",
       "date                                    \n",
       "2020-01-01  2.928244      1            1\n",
       "2020-01-02  4.866145      1            2\n",
       "2020-01-03  4.425159      1            3"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create feature day of year\n",
    "# ==============================================================================\n",
    "data['day_of_year'] = data.index.day_of_year\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# B-spline functions\n",
    "# ==============================================================================\n",
    "def spline_transformer(period, degree=3, extrapolation=\"periodic\"):\n",
    "    \"\"\"\n",
    "    Returns a transformer that applies B-spline transformation.\n",
    "    \"\"\"\n",
    "    return SplineTransformer(\n",
    "               degree        = degree,\n",
    "               n_knots       = period + 1,\n",
    "               knots         = 'uniform',\n",
    "               extrapolation = extrapolation,\n",
    "               include_bias  = True\n",
    "           ).set_output(transform=\"pandas\")\n",
    "\n",
    "splines_month = spline_transformer(period=12).fit_transform(data[['day_of_year']])\n",
    "splines_month.columns = [f\"spline{i}\" for i in range(len(splines_month.columns))]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph below shows the 12 spline functions generated using the day of the year as input. Since 12 splines are created with knots evenly distributed along the range 1 to 365 (day_of_year), each curve represents the proximity to the beginning of a particular month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "spline0    2020-12-01\n",
       "spline1    2020-01-01\n",
       "spline2    2020-01-31\n",
       "spline3    2020-03-02\n",
       "spline4    2020-04-01\n",
       "spline5    2020-05-02\n",
       "spline6    2020-06-01\n",
       "spline7    2020-07-02\n",
       "spline8    2020-08-01\n",
       "spline9    2020-08-31\n",
       "spline10   2020-10-01\n",
       "spline11   2020-10-31\n",
       "dtype: datetime64[ns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Location of the maximum value of each spline\n",
    "# ==============================================================================\n",
    "splines_month.idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jaesc2\\AppData\\Local\\Temp\\ipykernel_41772\\1440543275.py:4: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared.\n",
      "  splines_month.head(365).plot(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot of the B-splines functions for the first 365 days\n",
    "# ==============================================================================\n",
    "fig, ax = plt.subplots(figsize=(7, 4))\n",
    "splines_month.head(365).plot(\n",
    "    ax       = ax,\n",
    "    subplots = True,\n",
    "    sharex   = True,\n",
    "    legend   = False,\n",
    "    yticks   = [],\n",
    "    title    = 'Splines functions for the first 365 days'\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>spline0</th>\n",
       "      <th>spline1</th>\n",
       "      <th>spline2</th>\n",
       "      <th>spline3</th>\n",
       "      <th>spline4</th>\n",
       "      <th>spline5</th>\n",
       "      <th>spline6</th>\n",
       "      <th>spline7</th>\n",
       "      <th>spline8</th>\n",
       "      <th>spline9</th>\n",
       "      <th>spline10</th>\n",
       "      <th>spline11</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>2.928244</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>4.866145</td>\n",
       "      <td>0.150763</td>\n",
       "      <td>0.665604</td>\n",
       "      <td>0.183628</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>4.425159</td>\n",
       "      <td>0.135904</td>\n",
       "      <td>0.662485</td>\n",
       "      <td>0.201563</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   y   spline0   spline1   spline2   spline3  spline4  \\\n",
       "date                                                                    \n",
       "2020-01-01  2.928244  0.166667  0.666667  0.166667  0.000000      0.0   \n",
       "2020-01-02  4.866145  0.150763  0.665604  0.183628  0.000006      0.0   \n",
       "2020-01-03  4.425159  0.135904  0.662485  0.201563  0.000047      0.0   \n",
       "\n",
       "            spline5  spline6  spline7  spline8  spline9  spline10  spline11  \n",
       "date                                                                         \n",
       "2020-01-01      0.0      0.0      0.0      0.0      0.0       0.0       0.0  \n",
       "2020-01-02      0.0      0.0      0.0      0.0      0.0       0.0       0.0  \n",
       "2020-01-03      0.0      0.0      0.0      0.0      0.0       0.0       0.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encoded data\n",
    "# ==============================================================================\n",
    "data_encoded_splines = pd.concat([data, splines_month], axis=1)\n",
    "data_encoded_splines = data_encoded_splines.drop(columns=['day_of_year', 'month'])\n",
    "data_encoded_splines.head(3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare forecasting results\n",
    "\n",
    "A non-informative lag is included so that the impact of cyclical features can be assessed without being obscured by the autoregressive component."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create forecaster\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterRecursive(\n",
    "                 estimator = HistGradientBoostingRegressor(random_state=123),\n",
    "                 lags      = [70]\n",
    "             )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "70f7ce6c2c5341bbb691e5146be7df25",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Backtest error using one hot encoding: 1.10\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0f11194a2b5b4c03a33bb3a0982be04b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Backtest error using sine/cosine encoding: 1.12\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4ffc6e40ac094e99bb3c51686687c8b2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Backtest error using spline encoding: 0.75\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train and validate a forecaster using each encoding method\n",
    "# ==============================================================================\n",
    "datasets = [\n",
    "    data_encoded_oh, data_encoded_sin_cos, data_encoded_splines\n",
    "]\n",
    "encoding_methods = [\n",
    "    'one hot encoding', 'sine/cosine encoding', 'spline encoding'\n",
    "]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 3))\n",
    "data_test['y'].plot(title=\"Time series\", label=\"test\", ax=ax)\n",
    "\n",
    "for i, data_encoded in enumerate(datasets):\n",
    "\n",
    "    cv = TimeSeriesFold(\n",
    "            steps              = 365,\n",
    "            initial_train_size = len(data_encoded.loc[:end_train]),\n",
    "            refit              = False,\n",
    "         )\n",
    "    metric, predictions = backtesting_forecaster(\n",
    "                              forecaster    = forecaster,\n",
    "                              y             = data_encoded['y'],\n",
    "                              exog          = data_encoded.drop(columns='y'),\n",
    "                              cv            = cv,\n",
    "                              metric        = 'mean_squared_error'\n",
    "                          )\n",
    "\n",
    "    print(f\"Backtest error using {encoding_methods[i]}: {metric.at[0, 'mean_squared_error']:.2f}\")\n",
    "    predictions['pred'].plot(label=encoding_methods[i], ax=ax)\n",
    "    ax.legend(labels=['test'] + encoding_methods)\n",
    "    \n",
    "plt.show();"
   ]
  }
 ],
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